Spatial confounding in Bayesian species distribution modeling

Species distribution models (SDMs) are currently the main tools to derive species niche estimates and spatially explicit predictions for species geographical distribution. However, unobserved environmental conditions and ecological processes may confound the model estimates if they have a direct impact on the species and, at the same time, they are correlated with the observed environmental covariates. This, so-called spatial confounding, is a general property of spatial models but it has not been studied in the context of SDMs before. Here we examine how the estimation accuracy of SDMs depends on the type of spatial confounding. We construct two simulation studies where we alter spatial structures of the observed and unobserved covariates and the level of dependence between them. We fit generalized linear models with and without spatial random effects applying Bayesian inference and record the bias induced to model estimates by spatial confounding. After this, we examine spatial confo...物种分布模型（Species Distribution Models, SDMs）是当前获取物种生态位估计以及物种地理分布空间显式预测的主流工具。然而，若未观测到的环境条件与生态过程既对物种存在直接影响，同时又与观测到的环境协变量存在相关性，则可能会对模型估计结果造成混杂，此即所谓的空间混杂。空间混杂是空间模型的普遍特性，但此前尚未在物种分布模型的研究语境中得到探讨。在此，我们探究物种分布模型的估计精度如何受空间混杂类型的影响。我们开展了两项模拟研究，通过调整观测协变量与未观测协变量的空间结构，以及二者间的依赖水平。我们采用贝叶斯推断，拟合了包含与不包含空间随机效应的广义线性模型，并记录了空间混杂对模型估计值所诱导产生的偏差。此后，我们对空间混杂……